Excellence in Research: De Novo Protein Modeling, Beyond Topology Determination Problem

三维结构对生物系统的行为有很大的影响。例如，蛋白质可以加速反应，识别和杀死病原体，或者将物质运送到细胞和传出细胞。蛋白质结构有助于决定其最终功能。病毒受到形成外层涂层的蛋白质的保护，从而产生病毒颗粒。病毒颗粒的结构有助于确定病毒滑过免疫系统并感染细胞的效率。机器学习将被用来试图理解蛋白质的三维结构和功能之间的关系。该项目有望为蛋白质建模和药物设计做出贡献。该项目还将为HBCU田纳西州立大学(TSU)的学生提供研究和教育机会。这将增加准备为新兴生物技术经济做出贡献的劳动力的多样性。大多数药物针对的是膜蛋白。传统的测定技术，如X射线结晶学和传统的计算技术，对于许多类型的蛋白质，如膜蛋白质、难以结晶的蛋白质和大分子蛋白质，都是不成功的。低温电子显微镜(CRYO-EM)是一种相对较新的生物物理技术，能够生成核糖体和病毒衣壳等大分子复合体的体积图像。通常，低温电磁图像都是大而有噪声的数据集。该项目将研究一种新的想法，即减少图像的大小，并使用几何图形模型来构建体积组件，该模型可以显著降低问题的复杂性。将利用先进的机器学习和体积成分的几何特性对体积成分进行分析和分类，以识别蛋白质的二级结构元素(SSE)。该项目的目标是在一个全自动的框架内开发有效的算法来识别SSE的空间信息。该项目有三个目标：1)建立一个基于几何图论的新的数学模型，以减小低温电磁图像的尺寸。2)开发高效的算法，包括卷积神经网络和其他机器学习方法，来处理、分析和提取蛋白质大分子的体积图像中的SSE。3)将新方法与现有的分子可视化和建模平台(如嵌合体)相结合。如果成功，这个项目可以通过提供一个对噪音不那么敏感并且没有分割的自动化框架，来推进最近在使用低温EM进行蛋白质建模方面开发的最先进的方法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Three-dimensional (3D) structure has a large impact on the behavior of biological systems. For example, proteins can speed up reactions, recognize and kill pathogens, or shuttle material into and out of a cell. Protein structure helps determine its ultimate function. Viruses are protected by proteins that form an outer coating, creating a viral particle. The structure of viral particles helps determine how effective a virus is in slipping past the immune system and infecting cells. Machine learning will be employed to try to understand the relationship between a protein's 3-D structure and its function. This project is expected to contribute to protein modeling and drug design. This project will also provide research and educational opportunities for students at Tennessee State University (TSU), an HBCU. This will increase the diversity of the workforce prepared to contribute to the emerging biotechnology economy.Most pharmaceutical drugs target membrane proteins. Conventional determination techniques such as X-ray crystallography and traditional computational techniques are unsuccessful with many types of proteins, such as membrane proteins, proteins that are hard to crystallize, and macromolecular proteins. Cryo-Electron Microscopy (cryo-EM) is a relatively new biophysics technique with the capability to generate volumetric images of macromolecular complexes and ensembles like ribosomes and viral capsids. Usually, cryo-EM images are large and noisy datasets. The project will investigate a novel idea to reduce the size of the image and build volume components using a geometric graph model that can drastically reduce the complexity of the problem. The volume components will be analyzed and classified to identify the secondary structure elements (SSEs) of proteins using advanced machine learning and geometric properties of the volume components. The goal of this project is to develop efficient algorithms in a fully automatic framework to identify the spatial information of the SSEs. The project has three objectives:1) Build a new mathematical model based on geometrical graph theory to reduce the size of cryo-EM images. 2) Develop efficient algorithms, including convolutional neural networks and other machine learning approaches, to process, analyze, and extract the SSEs from the volumetric images of protein macromolecule. 3) Integrate the new methods with an existing visualization and modeling platform for molecules like Chimera. If successful, this project could advance the state-of-the-art methods recently developed in protein modeling using cryo-EM by providing an automated framework that is less sensitive to noise and is segmentation-free.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.